Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Attentional Biases in Value-Based Decision-Making

Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

Facts and Fictions: Feminist Literary Criticism and Cultural Critique, 1968-2012

"Facts and Fictions: Feminist Literary Criticism and Cultural Critique, 1968-2012" is a critical history of the unfolding of feminist literary study in the US academy. It contributes to current scholarly efforts to revisit the 1970s by reconsidering often-repeated narratives about the critical naivety of feminist literary criticism in its initial articulation. As the story now goes, many of the most prominent feminist thinkers of the period engaged in unsophisticated literary analysis by conflating lived social reality with textual representation when they read works of literature as documentary evidence of real life. As a result, the work of these "bad critics," particularly Kate Millett and Andrea Dworkin, has not been fully accounted for in literary critical terms.

This dissertation returns to Dworkin and Millett's work to argue for a different history of feminist literary criticism. Rather than dismiss their work for its conflation of fact and fiction, I pay attention to the complexity at the heart of it, yielding a new perspective on the history and persistence of the struggle to use literary texts for feminist political ends. Dworkin and Millett established the centrality of reality and representation to the feminist canon debates of "the long 1970s," the sex wars of the 1980s, and the more recent feminist turn to memoir. I read these productive periods in feminist literary criticism from 1968 to 2012 through their varied commitment to literary works.

Chapter One begins with Millett, who de-aestheticized male-authored texts to treat patriarchal literature in relation to culture and ideology. Her mode of literary interpretation was so far afield from the established methods of New Criticism that she was not understood as a literary critic. She was repudiated in the feminist literary criticism that followed her and sought sympathetic methods for reading women's writing. In that decade, the subject of Chapter Two, feminist literary critics began to judge texts on the basis of their ability to accurately depict the reality of women's experiences.

Their vision of the relationship between life and fiction shaped arguments about pornography during the sex wars of the 1980s, the subject of Chapter Three. In this context, Dworkin was feminism's "bad critic." I focus on the literary critical elements of Dworkin's theories of pornographic representation and align her with Millett as a miscategorized literary critic. In the decades following the sex wars, many of the key feminist literary critics of the founding generation (including Dworkin, Jane Gallop, Carolyn Heilbrun, and Millett) wrote memoirs that recounted, largely in experiential terms, the history this dissertation examines. Chapter Four considers the story these memoirists told about the rise and fall of feminist literary criticism. I close with an epilogue on the place of literature in a feminist critical enterprise that has shifted toward privileging theory.

Brain activity during episodic retrieval of autobiographical and laboratory events: an fMRI study using a novel photo paradigm.

Functional neuroimaging studies of episodic memory retrieval generally measure brain activity while participants remember items encountered in the laboratory ("controlled laboratory condition") or events from their own life ("open autobiographical condition"). Differences in activation between these conditions may reflect differences in retrieval processes, memory remoteness, emotional content, retrieval success, self-referential processing, visual/spatial memory, and recollection. To clarify the nature of these differences, a functional MRI study was conducted using a novel "photo paradigm," which allows greater control over the autobiographical condition, including a measure of retrieval accuracy. Undergraduate students took photos in specified campus locations ("controlled autobiographical condition"), viewed in the laboratory similar photos taken by other participants (controlled laboratory condition), and were then scanned while recognizing the two kinds of photos. Both conditions activated a common episodic memory network that included medial temporal and prefrontal regions. Compared with the controlled laboratory condition, the controlled autobiographical condition elicited greater activity in regions associated with self-referential processing (medial prefrontal cortex), visual/spatial memory (visual and parahippocampal regions), and recollection (hippocampus). The photo paradigm provides a way of investigating the functional neuroanatomy of real-life episodic memory under rigorous experimental control.

Ultra High Pressure Hydrogen Studies

Hydrogen has been called the fuel of the future, and as it’s non- renewable counterparts become scarce the economic viability of hydrogen gains traction. The potential of hydrogen is marked by its high mass specific energy density and wide applicability as a fuel in fuel cell vehicles and homes. However hydrogen’s volume must be reduced via pressurization or liquefaction in order to make it more transportable and volume efficient. Currently the vast majority of industrially produced hydrogen comes from steam reforming of natural gas. This practice yields low-pressure gas which must then be compressed at considerable cost and uses fossil fuels as a feedstock leaving behind harmful CO and CO2 gases as a by-product. The second method used by industry to produce hydrogen gas is low pressure electrolysis. In comparison the electrolysis of water at low pressure can produce pure hydrogen and oxygen gas with no harmful by-products using only water as a feedstock, but it will still need to be compressed before use. Multiple theoretical works agree that high pressure electrolysis could reduce the energy losses due to product gas compression. However these works openly admit that their projected gains are purely theoretical and ignore the practical limitations and resistances of a real life high pressure system. The goal of this work is to experimentally confirm the proposed thermodynamic gains of ultra-high pressure electrolysis in alkaline solution and characterize the behavior of a real life high pressure system.